eprintid: 71769
rev_number: 10
eprint_status: archive
userid: 12460
dir: disk0/00/07/17/69
datestamp: 2025-07-11 08:09:03
lastmod: 2025-07-11 08:09:03
status_changed: 2025-07-11 08:09:03
type: thesis
metadata_visibility: show
contact_email: muh.khabib@uin-suka.ac.id
creators_name: Refina Nur Asmia, NIM.: 21106010038
title: KLASIFIKASI KANKER PAYUDARA MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE
ispublished: pub
subjects: 560
divisions: jur_mat
full_text_status: restricted
keywords: Support Vector Machine (SVM), Kanker Payudara, Kernel Linier, Kernel Polinomial, Kernel RBF, Kernel Sigmoid, Akurasi
note: Muhamad Rashif Hilmi, S.Si., M.Sc. dan Deddy Rahmadi, M.Sc.
abstract: Breast cancer is one of the non-communicable diseases that causes death worldwide, particularly among women. Early detection and classification of the survival status or death of breast cancer patients are crucial in determining appropriate treatment. This study aims to examine the performance steps of the SVM algorithm, assess the performance of the SVM algorithm using linear, polynomial, RBF, and sigmoid kernel methods, and identify the best-performing SVM kernel in this classification. The data used in this study were obtained from the Surveillance, Epidemiology, and End Results (SEER) Program, November 2017 version, which includes female patients diagnosed with infiltrating duct and lobular carcinoma breast cancer between 2006 and 2010. There are 14 independen variables, namely: age, race, marital status, t stage, n stage, 6th stage, grade, a stage, tumor size, estrogen status, progesterone status, regional node examined, regional node positive, and survival months. The dependen variable is status. The method used involves four SVM kernels: linear, polynomial, Radial Basis Function (RBF), and sigmoid. The accuracy results obtained are as follows: linear kernel: 91.105%, polynomial kernel: 90.384%, RBF kernel: 91.225%, and sigmoid kernel: 90.384%. The best performance result in this classification was achieved by the RBF kernel.
date: 2025-06-05
date_type: published
pages: 85
institution: UIN SUNAN KALIJAGA YOGYAKARTA
department: FAKULTAS SAINS DAN TEKNOLOGI
thesis_type: skripsi
thesis_name: other
citation:   Refina Nur Asmia, NIM.: 21106010038  (2025) KLASIFIKASI KANKER PAYUDARA MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE.  Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.   
document_url: https://digilib.uin-suka.ac.id/id/eprint/71769/1/21106010038_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
document_url: https://digilib.uin-suka.ac.id/id/eprint/71769/2/21106010038_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf